Broadly neutralizing antibodies targeting important pathogens, such as HIV-1, influenza, and Malaria, have been isolated from probe sorting or other experimental techniques. However, often time there is still room for improvement in terms of neutralization potency and breadth for these antibodies. Furthermore, many properties relating to the developability of the antibodies, such as solubility, immunogenicity, polyreactivity, and pharmacokinetics, needs to be optimized before they can be manufactured and tested in clinical settings. Here we apply computational structure-based design and machine learning predictors to improve the properties of the antibodies in the VRC pipeline.